Forensic Data Linking to Consumer Transaction Profiles
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Solution Overview
Problem
Law enforcement agencies face challenges in linking forensic data from computing devices to transaction history and purchase data, especially when multiple users are involved or when devices are stolen, as existing methods struggle to identify users and retrieve comprehensive transaction information.
Innovation Solution
A system and method for linking forensic data to transaction history by storing consumer profiles with identifiers and transaction data, receiving forensic profiles from computing devices, and identifying corresponding consumer profiles to transmit relevant transaction data or demographic characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to identify users based on forensic data (such as looking up phone numbers or IP addresses), then some user identification may be achieved, but the identification fails when multiple users are involved or when devices are stolen
Solution Approach 1:
The patent segments the identification process into multiple independent analysis dimensions: device characteristics (hardware ID, OS version, screen resolution), usage patterns (app installation patterns, browsing behavior, typing patterns), and temporal analysis. By dividing the identification problem into these separate segments, the system can cross-validate multiple factors rather than relying on a single identifier that may be shared or stolen.
Solution Approach 2:
The patent changes the parameters used for identification from static identifiers (phone numbers, IP addresses) to dynamic behavioral parameters (typing speed, app usage patterns, browsing preferences, purchase behavior). These parameters change over time and are unique to individual users, making them more reliable for identification even when devices are stolen or shared.
2Loss of information
If forensic analysis focuses only on device data, then device-related information can be obtained, but comprehensive transaction history and purchase data remain unavailable
Solution Approach 1:
The patent introduces an intermediary linkage system that connects forensic data with transaction databases through matching algorithms. The intermediary layer processes device characteristics and usage patterns, then uses these as keys to query transaction history databases, thereby bridging the gap between forensic analysis and comprehensive transaction data without requiring direct integration of all systems.
Solution Approach 2:
The patent creates a universal identification framework that can be applied across multiple data sources (device forensics, transaction records, behavioral data, purchase history). This multi-functional system uses the same core matching algorithms to link various types of data, reducing overall system complexity through standardization rather than creating separate complex integration paths for each data type.
3Measurement precision
If detailed forensic analysis is performed to identify users accurately, then user identification improves, but the time and resources required for analysis increase
Solution Approach 1:
The patent performs preliminary analysis by first extracting and categorizing device characteristics (hardware ID, OS version, screen resolution) and basic usage patterns, then uses these preliminary results to quickly filter and narrow down potential users before conducting more detailed behavioral analysis. This staged approach reduces overall analysis time by eliminating unnecessary deep-dive investigations into clearly mismatched candidates.
Solution Approach 2:
The patent replaces manual or sequential forensic analysis methods with automated computational algorithms that can simultaneously process multiple identification parameters (device characteristics, usage patterns, temporal data). This substitution of mechanical/sequential analysis with parallel computational processing dramatically reduces analysis time while maintaining or improving identification accuracy.
Data Source
AI summary
A method for linking forensic data to transaction history includes: storing a plurality of consumer profiles, each profile including data related to a consumer including a consumer identifier associated with the related consumer and a plurality of transaction data entries, each entry including data related to a payment transaction involving the consumer including transaction data; receiving a forensic profile, the profile including forensic data obtained from a computing device; identifying a correspondence between the received forensic data and transaction data included in the stored transaction data entries to obtain a specific consumer identifier; identifying a specific consumer profile where the included consumer identifier corresponds to the specific consumer identifier; and transmitting at least the transaction data included in one or more transaction data entries included in the identified specific consumer profile.